Evaluation of effective features in the diagnosis of Covid‐19 infection from routine blood tests with multilayer perceptron neural network: A cross‐sectional study

Abstract Background and Aim Coronavirus is an infectious disease that is now known as an epidemic, early and accurate diagnosis helps the patient receive more care. The aim of this study is to investigate Covid‐19 using blood tests and multilayer perceptron neural network and affective factors in improving and preventing Covid‐19. Methods This cross‐sectional study was performed on 200 patients referred to Sina Hospital, Tehran, Iran, who were confirmed cases of Covid‐19 by computerized tomography‐scan analysis between 2 March 2020 to 5 April 2020. After verification of lung involvement, blood sampling was done to separate the sera for C‐reactive protein (CRP), magnesium (Mg), lymphocyte percentage, and vitamin D analysis in healthy and unhealthy people. Blood samples from healthy and sick people were applied to the multilayer perceptron network for 70% of the data for training and 30% for testing. Result By examining the features, it was found that in patients with Covid‐19, there was a significant relationship between increased CRP and decreased lymphocyte levels, and increased Mg (p < 0.01). In these patients, the amount of CRP and Mg in women and the number of lymphocytes and vitamin D in men were significantly higher (p < 0.01). Conclusion The important advantage of using a multilayer perceptron neural network is to speed up the diagnosis and treatment.


| INTRODUCTION
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic, called Covid-19, has spread with unprecedented severity and extent around the world. After infecting countries, governments around the world have taken serious measures, such as quarantining hundreds of millions of people in different parts of the world, to combat the spread of Covid-19 infection. 1 When an infected person is found, he or she is isolated and treated for recovery. 2 The patient must be isolated to prevent transmission. Therefore, it is important to early identify and give treatment to these subsets of patients to reduce the disease severity and improve the outcomes of . Clinical studies demonstrated that altered levels of some blood markers might be linked with the degree of severity and mortality of patients with Covid-19. 3 Parameters extracted from the blood test in this article are as follows: The recent review shows that supplementation with multiple micronutrients plays immune-supporting roles that modulate the immune function and reduce the risk of infection. In this vein, micronutrients such as vitamins C and D and zinc have the strongest evidence for immune support. 5

| Lymphocytes
Lymphocytes are a type of white blood cell and are an important component of the immune system. These cells are made in the bone marrow and are found in blood and lymph fluid. The immune system is a complex network of cells known as immune cells, including lymphocytes. These cells work together to protect the body against invading agents, such as bacteria, viruses, and cancer cells, all of which threaten the body's health.
Several studies have determined a correlation between the disease and lymphopenia, a condition defined by abnormally low counts of lymphocytes. A closer look at Covid-19 patients suffering from lymphopenia almost always exhibit significant decreases in T cell counts. 6 Natural killer cells and cytotoxic T cells are essential in the control of the viral infection. In recent studies (by Huang et al. and Yang et al. in 2020), 7,8 it was shown that about 85% of severely ill patients with Covid-19 are suffering the lymphopenia. 9 1.3 | C-reactive protein (CRP) Measurement of CRP in a medical diagnostic test is a nonspecific test to diagnose rheumatism and arthritis. In the event of microbial infection and infectious agents, the amount of this protein in the blood serum increases and becomes so-called positive.
Novel coronavirus (2019) seems to increase CRP levels significantly, due to inflammatory reactions and related tissue destruction was also seen in the SARS epidemic in 2002. 10 Higher concentration indicates more severe disease-linked to lung damage and worse prognosis. 11

| Magnesium (Mg)
Most of the Mg in the body is in the intracellular fluid, and some of it is in the bone and, as a cofactor, regulates the activity of many enzymes. Mg regulates the metabolism and synthesis of carbohydrates, proteins, and nucleic acids. The function of many organs, such as neuromuscular tissue, is also dependent on Mg.
Mg also has anti-inflammation, anti-oxidation, antispasm, vasodilation, and neuroprotection. Mg is expected to play an active role in clinical practice in the prevention and treatment of Covid-19.
Therefore, the possibility of the use of Mg supplementation in the prevention and treatment of  Patients with Covid-19 symptoms were screened for real-time reverse transcription polymerase chain reaction on respiratory specimens. In this article, two models of machine learning have been used, the accuracy of diagnosing the disease is between 82% and 86%, and the sensitivity of the learning machine in diagnosing the disease is between 92% and 95%.
In Hastie et al., 13 CRP levels were collected, and the diameter of the largest lung lesion was measured. Patients were divided into four groups: mild, moderate, severe, and critical. CRP level and diameter of the largest lung lesion in the moderate group were higher than those in the mild group, higher in the severe group than in the moderate group, and higher in the critical group than in the severe group. Therefore, in the early stages of The conceptual structures and operating systems introduced by Jamshidi et al. 16 indicate that AI-based techniques are suitable for coping with and detecting Covid-19. It includes Covid-19 diagnostic systems such as recurrent neural networks, long short-term memory, generative adversarial networks, and extreme learning machines.
Geographical issues, high-risk individuals, and cognition and radiology are the main problems of Covid-19 and have been studied and discussed in this work.
In this paper, first, with the help of a multilayer perceptron (MLP) neural network, four different parameters of blood testing are examined, the network is trained and tested, and then the accuracy of the network is calculated. With the help of feature selection methods, effective features are distinguished from ineffective ones. This method helps experts and doctors diagnose the disease more quickly and accurately to fight the virus. Success in combating Covid-19 for its eventual elimination depends on the methods and tools used to detect it early and prevent it from spreading. Achieving the desired goals and achieving the salvation of most lives depends on early detection. In this study, Covid-19 infection was diagnosed using an MLP network and features extracted from a blood test along with statistical tests. A block diagram of how to use the MLP network is shown in Figure 1, which generally includes four steps: (1) collecting blood samples from patients and extracting parameters, (2) forming and constructing a feature vector for healthy people and unhealthy,

| MLP network
An MLP network is a neural network used to segment data. A typical MLP consists of input and output layers, and one or more intermediate layers. First, the network structure is formed, and then the neurons are connected by weights, and the network is trained. Figure 2 shows the structure of this network. 17 In this paper, the number of MLP input and output nodes is 4 and 2, respectively.
The number of nodes in each of the two middle layers is equal to 30.
The inputs are multiplied by the weights that are calculated by the network at each node. Then the output of the network is calculated.
The network was trained and tested with two classes so that the output contains one of two numbers as follows: If the output becomes zero, it means there is no disease, and if the output becomes one, it means there is a disease.

| Diagnosis feature selection
Due to the good performance of UTA, 1 (FP1, FN1). Then, calculate the difference between the sums of the above two.
There are three ways to error: 1. Error = 0: The error does not change, so the attribute is not effective.
2. Error > 0: The error increases, so the feature is effective.
3. Error < 0: Error is reduced, so the feature is not effective, but also malicious.  The mean difference between healthy and sick individuals for different clinical values is shown using Figure 3.

The difference in values obtained for vitamin D in healthy
individuals and Covid-19 individuals was not significant, but the values related to CRP, Mg, and lymphocyte percentage in these two groups with a p-value of 0.0 were considered significant. Table 4 shows the differences in this Mg, CRP, lymphocyte, and vitamin D parameters between healthy individuals and Covid-19 individuals.  Table 5. Figure 4 shows the difference between the output of an MLP network and the actual output after 940 iterations. As can be seen, the error is decreasing.
The results show that the MLP network can detect Covid-19 individuals from blood samples with relatively good accuracy.

| Extract properties
After completing the training and testing phase by the multilayer perceptron classifier, using the UTA algorithm, 1   (1), if the error is zero, the property is not effective. If it is greater than zero, it is effective, and if it is less than zero, it is destructive. Table 6 shows that the error is positive in the first and last two cases, that is, the CRP parameter with the highest error is 59, the most effective parameter, and then the lymphocyte parameter with an error value of 18 is affected in the second order. Mg and vitamin D parameters are not effective properties.

| CONCLUSION
The present study shows that the characteristics of CRP and lymphocytes extracted from blood tests can be used for a more accurate and early diagnosis of people with Covid-19. The proposed method uses a new feature matrix that distinguishes infected people from healthy individuals with acceptable accuracy of 99.1667% with the help of an MLP network. The features used in this algorithm could play a good role in diagnosing the disease. Figure 5  Also, in this study, body mass index was evaluated, and it was observed that a higher rate is associated with longer hospital stays.
Although the number of subjects in this study was small, the results In the present study, a significant and direct relationship was Hedieh Moradi Tabriz: Writingreview & editing.

ACKNOWLEDGMENTS
The authors acknowledge the Sina Hospital for the partial support of this work.

CONFLICT OF INTEREST
The authors declare no conflict of interest.

DATA AVAILABILITY STATEMENT
The data are not publicly available due to privacy or ethical restrictions.

ETHICS STATEMENT
This study was approved by the ethics committee of the Tehran

University of Medical Sciences (Sina Clinical-Research Center) in
Tehran, Iran, also informed consent was obtained for those eligible to enter the study.

TRANSPARENCY STATEMENT
The lead author Hamid Reza Akbari-Hasanjani affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.